Hands on session: Data extraction: scripts will be provided to gather data from
Twitter and Facebook.

Part 2: Using semantics to represents data from Social Networking Sites

Theory:

Using semantics to represent data from SNS: motivation

Data unification

Portable analysis models

The Social Semantic Web Stack

Representing content

Representing people

Representing relations

SIOC: the Semantic Interlinked Online Communities model

Applying and extending SIOC

Part 3: Using semantics to understand social media conversations
Theory:

Topic analysis using explicit semantics: exploiting entities and topics
extracted via TextRazor to understand the feedback of Open University
students in Facebook open groups

Topic analysis using implicit semantics: elicitation of latent topics from
Twitter data in the entertainment domain

Sentiment analysis using explicit semantics: using semantic concepts to
reduce Twitter data sparsity and enhance the accuracy of sentiment
classifiers

Emotion analysis using a rule-­based system.

Hands on session: Data annotation and analysis

Using DBPedia to extract entities from Twitter data

Using Latent Dirichlet Allocation to extract topics from Twitter data

Topic Analysis, which key topics emerge from the data?

Hands on session: Emotion Analysis

Extracting emotions from Twitter data

Part 4: Using semantics to understand user behaviour
Theory:

User roles in online communities: who are the leaders, the celebrities, the
daily contributors and the ignored users?

The Open University Behaviour Analysis Ontology

Monitoring behaviour over time using SPIN rules

Abstract

Social networks generate major economic value and form pivotal parts of
commercial services, advertisement, entertainment, etc. Multiple tools and
technologies have emerged in the last few years that aim to monitor and analyse
data from these networks in order to exploit their value. This tutorial aims to
provide a comprehensive overview of where and how semantic information has
been used to represent and analyse social networking data. The presented
research and development covers different use cases and applications including
education, e-­‐government and business cooperation. With the help of hands on
session attendees will make use of some of these technologies to collect and
analyse data from popular social networking sites, such as Facebook or Twitter.
Semantic technologies will be applied to perform topic and sentiment analysis
over these data.

Contents

The explosive growth of social networking sites continues in all areas of society,
and their use is now widespread in social, business, scientific and public service
domains, enabling community members to share ideas, knowledge and opinions.
Beyond high profile public social networking sites, e.g. Facebook or Twitter,
social networks now generate major economic value to businesses and can form
pivotal parts of corporate expertise management, corporate marketing, product
support and targeted advertising. A recent report by McKinsey 1 estimates that
between 900 and 1,300 billion dollars in annual value could be obtained if social
media is fully exploited.

With the purpose of exploiting the value of these networks multiple technologies
and analysis methods have been developed in the last few years. In this tutorial
we focus on those methods that exploit semantic information, not only to better
model that data from these networks, but also to provide more comprehensive
and flexible analyses. The studied semantic analysis methods make use of both,
implicit and explicit semantic information. While explicit semantic analysis
methods make use of ontologies, knowledge bases and linked data, implicit
semantic analysis methods exploit natural language features to identify the
entities and relations emerging from the data. Once semantic information is
identified and selected it is used to enhance the flexibility and performance of a
variety of analyses.

In this tutorial we will explore how semantic information, both implicit and
explicit, has been used in the literature to model and integrate data across communities,
as well as to analyse this data. Some of these analyses include
understanding the users’ behaviour and their influence within the communities,
detecting users’ sentiment and emotions and identifying the topics that emerge
from the users’ conversations.

Examples of the application of these analyses over different practical uses cases
will be presented. These uses cases include (i) the analysis of Facebook data to
identify the issues and concerns of Open University (OU) students regarding
particular OU courses, (ii) the analysis of Twitter data to identify the key topics
and opinions of citizens regarding policy related topics, (iii) the elicitation of
latent topics from Twitter data in the entertainment domain, and (iv) the
analysis of private intranets, such as IBM connections and SAP Community
Network, to monitor the behaviour of their users.

García-­Silva, A. Rodríguez-­Doncel V. and Corcho, O. Semantic Characterization of
Tweets Using Topic Models: A Use Case in the Entertainment Domain.
International Journal on Semantic Web and Information Systems (In press)

Required Knowledge

Basic knowledge in Semantic Web and Social Networks
may allow better following the tutorial and gaining more benefits from it. A
Twitter and Facebook accounts are required for the first hands-­on session.